Workload Decomposition Strategies for Shared Memory Parallel Systems with OpenMP | Zendy

Beniamino Di Martino | Zendy; S. Briguglio | Zendy; G. Vlad | Zendy; G. Fogaccia | Zendy

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Workload Decomposition Strategies for Shared Memory Parallel Systems with OpenMP

Author(s) -

Beniamino Di Martino,

S. Briguglio,

G. Vlad,

G. Fogaccia

Publication year - 2001

Publication title -

scientific programming

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.269

H-Index - 36

eISSN - 1875-919X

pISSN - 1058-9244

DOI - 10.1155/2001/891073

Subject(s) - computer science , shared memory , parallel computing , decomposition , distributed memory , workload , implementation , distributed shared memory , task (project management) , restructuring , programming paradigm , uniform memory access , programming language , distributed computing , memory management , operating system , overlay , finance , economics , biology , ecology , management

A crucial issue in parallel programming (both for distributed and shared memory architectures) is work decomposition. Work decomposition task can be accomplished without large programming effort with use of high-level parallel programming languages, such as OpenMP. Anyway particular care must still be payed on achieving performance goals. In this paper we introduce and compare two decomposition strategies, in the framework of shared memory systems, as applied to a case study particle in cell application. A number of different implementations of them, based on the OpenMP language, are discussed with regard to time efficiency, memory occupancy, and program restructuring effort

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